And remember to set eval=TRUE
plot_ly() and ggplotly() functionsplot_geo()We will work with COVID data downloaded from the New York Times. The dataset consists of COVID-19 cases and deaths in each US state during the course of the COVID epidemic.
The objective of this lab is to explore relationships between cases, deaths, and population sizes of US states, and plot data to demonstrate this
## data extracted from New York Times state-level data from NYT Github repository
# https://github.com/nytimes/covid-19-data
## state-level population information from us_census_data available on GitHub repository:
# https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data
### FINISH THE CODE HERE ###
# load COVID state-level data from NYT
cv_states <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"))
### FINISH THE CODE HERE ###
# load state population data
state_pops <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv"))
state_pops$abb <- state_pops$state
state_pops$state <- state_pops$state_name
state_pops$state_name <- NULL
### FINISH THE CODE HERE
cv_states <- merge(cv_states, state_pops, by= "state")
head, and tail of the datadim(cv_states)
## [1] 32250 9
head(cv_states)
## state date fips cases deaths geo_id population pop_density abb
## 1 Alabama 2020-06-10 1 21989 744 1 4887871 96.50939 AL
## 2 Alabama 2021-10-31 1 832047 15573 1 4887871 96.50939 AL
## 3 Alabama 2021-05-26 1 542831 11138 1 4887871 96.50939 AL
## 4 Alabama 2020-04-19 1 4903 160 1 4887871 96.50939 AL
## 5 Alabama 2021-07-07 1 552911 11387 1 4887871 96.50939 AL
## 6 Alabama 2020-06-21 1 30021 839 1 4887871 96.50939 AL
tail(cv_states)
## state date fips cases deaths geo_id population pop_density abb
## 32245 Wyoming 2020-12-01 56 33805 230 56 577737 5.950611 WY
## 32246 Wyoming 2021-08-15 56 68272 793 56 577737 5.950611 WY
## 32247 Wyoming 2021-03-16 56 55352 693 56 577737 5.950611 WY
## 32248 Wyoming 2021-04-12 56 56988 701 56 577737 5.950611 WY
## 32249 Wyoming 2021-04-01 56 56389 700 56 577737 5.950611 WY
## 32250 Wyoming 2020-11-30 56 33305 215 56 577737 5.950611 WY
str(cv_states)
## 'data.frame': 32250 obs. of 9 variables:
## $ state : chr "Alabama" "Alabama" "Alabama" "Alabama" ...
## $ date : IDate, format: "2020-06-10" "2021-10-31" ...
## $ fips : int 1 1 1 1 1 1 1 1 1 1 ...
## $ cases : int 21989 832047 542831 4903 552911 30021 117242 809485 134417 547135 ...
## $ deaths : int 744 15573 11138 160 11387 839 2037 14869 2285 11252 ...
## $ geo_id : int 1 1 1 1 1 1 1 1 1 1 ...
## $ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
## $ pop_density: num 96.5 96.5 96.5 96.5 96.5 ...
## $ abb : chr "AL" "AL" "AL" "AL" ...
# format the date
cv_states$date <- as.Date(cv_states$date, format="%Y-%m-%d")
# format the state and state abbreviation (abb) variables
state_list <- unique(cv_states$state)
cv_states$state <- factor(cv_states$state, levels = state_list)
abb_list <- unique(cv_states$abb)
cv_states$abb <- factor(cv_states$abb, levels = abb_list)
### FINISH THE CODE HERE
# order the data first by state, second by date
cv_states = cv_states[order(cv_states$state, cv_states$date),]
# Confirm the variables are now correctly formatted
str(cv_states)
## 'data.frame': 32250 obs. of 9 variables:
## $ state : Factor w/ 52 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ date : Date, format: "2020-03-13" "2020-03-14" ...
## $ fips : int 1 1 1 1 1 1 1 1 1 1 ...
## $ cases : int 6 12 23 29 39 51 78 106 131 157 ...
## $ deaths : int 0 0 0 0 0 0 0 0 0 0 ...
## $ geo_id : int 1 1 1 1 1 1 1 1 1 1 ...
## $ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
## $ pop_density: num 96.5 96.5 96.5 96.5 96.5 ...
## $ abb : Factor w/ 52 levels "AL","AK","AZ",..: 1 1 1 1 1 1 1 1 1 1 ...
head(cv_states)
## state date fips cases deaths geo_id population pop_density abb
## 215 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL
## 428 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL
## 69 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL
## 496 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL
## 341 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL
## 53 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL
tail(cv_states)
## state date fips cases deaths geo_id population pop_density abb
## 31735 Wyoming 2021-11-06 56 105318 1243 56 577737 5.950611 WY
## 31791 Wyoming 2021-11-07 56 105318 1243 56 577737 5.950611 WY
## 31967 Wyoming 2021-11-08 56 105990 1243 56 577737 5.950611 WY
## 31942 Wyoming 2021-11-09 56 106287 1298 56 577737 5.950611 WY
## 31961 Wyoming 2021-11-10 56 106698 1298 56 577737 5.950611 WY
## 32016 Wyoming 2021-11-11 56 106698 1298 56 577737 5.950611 WY
# Inspect the range values for each variable. What is the date range? The range of cases and deaths?
head(cv_states)
## state date fips cases deaths geo_id population pop_density abb
## 215 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL
## 428 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL
## 69 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL
## 496 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL
## 341 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL
## 53 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL
summary(cv_states)
## state date fips cases
## Washington : 661 Min. :2020-01-21 Min. : 1.00 Min. : 1
## Illinois : 658 1st Qu.:2020-08-03 1st Qu.:16.00 1st Qu.: 31774
## California : 657 Median :2021-01-05 Median :29.00 Median : 146459
## Arizona : 656 Mean :2021-01-05 Mean :29.78 Mean : 386609
## Massachusetts: 650 3rd Qu.:2021-06-09 3rd Qu.:44.00 3rd Qu.: 481786
## Wisconsin : 646 Max. :2021-11-11 Max. :72.00 Max. :4993930
## (Other) :28322
## deaths geo_id population pop_density
## Min. : 0 Min. : 1.00 Min. : 577737 Min. : 1.292
## 1st Qu.: 621 1st Qu.:16.00 1st Qu.: 1805832 1st Qu.: 43.659
## Median : 2658 Median :29.00 Median : 4468402 Median : 107.860
## Mean : 7155 Mean :29.78 Mean : 6433897 Mean : 422.513
## 3rd Qu.: 8432 3rd Qu.:44.00 3rd Qu.: 7535591 3rd Qu.: 229.511
## Max. :73132 Max. :72.00 Max. :39557045 Max. :11490.120
## NA's :609
## abb
## WA : 661
## IL : 658
## CA : 657
## AZ : 656
## MA : 650
## WI : 646
## (Other):28322
min(cv_states$date)
## [1] "2020-01-21"
max(cv_states$date)
## [1] "2021-11-11"
new_cases and new_deaths and correct outliersAdd variables for new cases, new_cases, and new deaths, new_deaths:
new_cases equal to the difference between cases on date i and date i-1, starting on date i=2Filter to dates after June 1, 2021
Use plotly for EDA: See if there are outliers or values that don’t make sense for new_cases and new_deaths. Which states and which dates have strange values?
Correct outliers: Set negative values for new_cases or new_deaths to 0
Recalculate cases and deaths as cumulative sum of updated new_cases and new_deaths
Get the rolling average of new cases and new deaths to smooth over time
Inspect data again interactively
# Add variables for new_cases and new_deaths:
for (i in 1:length(state_list)) {
cv_subset = subset(cv_states, state == state_list[i])
cv_subset = cv_subset[order(cv_subset$date),]
# add starting level for new cases and deaths
cv_subset$new_cases = cv_subset$cases[1]
cv_subset$new_deaths = cv_subset$deaths[1]
### FINISH THE CODE HERE
for (j in 2:nrow(cv_subset)) {
cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j-1]
cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j-1]
}
# include in main dataset
cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases
cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths
}
# Focus on recent dates
cv_states <- cv_states %>% dplyr::filter(date >= "2021-06-01")
### FINISH THE CODE HERE
# Inspect outliers in new_cases using plotly
p1<-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p1)
p1<-NULL # to clear from workspace
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)
p2<-NULL # to clear from workspace
# set negative new case or death counts to 0
cv_states$new_cases[cv_states$new_cases<0] = 0
cv_states$new_deaths[cv_states$new_deaths<0] = 0
# Recalculate `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths`
for (i in 1:length(state_list)) {
cv_subset = subset(cv_states, state == state_list[i])
# add starting level for new cases and deaths
cv_subset$cases = cv_subset$cases[1]
cv_subset$deaths = cv_subset$deaths[1]
### FINISH CODE HERE
for (j in 2:nrow(cv_subset)) {
cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j-1]
cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j-1]
}
# include in main dataset
cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases
cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths
}
# Smooth new counts
cv_states$new_cases = zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') %>% round(digits = 0)
cv_states$new_deaths = zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') %>% round(digits = 0)
# Inspect data again interactively
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)
#p2=NULL
Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:
per100k = cases per 100,000 populationnewper100k= new cases per 100,000deathsper100k = deaths per 100,000newdeathsper100k = new deaths per 100,000Add a “naive CFR” variable representing deaths / cases on each date for each state
Create a dataframe representing values on the most recent date, cv_states_today, as done in lecture
### FINISH CODE HERE
# add population normalized (by 100,000) counts for each variable
cv_states$per100k = as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1))
cv_states$newper100k = as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1))
cv_states$deathsper100k = as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1))
cv_states$newdeathsper100k = as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1))
# add a naive_CFR variable = deaths / cases
cv_states = cv_states %>% mutate(naive_CFR = round((deaths*100/cases),2))
# create a `cv_states_today` variable
cv_states_today = subset(cv_states, date==max(cv_states$date))
plot_ly()plot_ly() representing pop_density vs. various variables (e.g. cases, per100k, deaths, deathsper100k) for each state on most recent date (cv_states_today)
hovermode = "compare"### FINISH CODE HERE
# pop_density vs. cases
cv_states_today %>%
plot_ly(x = ~pop_density, y = ~cases,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
# filter out "District of Columbia" since it's an outlier
cv_states_today_filter <- cv_states_today %>% filter(state!="District of Columbia")
# pop_density vs. cases after filtering
cv_states_today_filter %>%
plot_ly(x = ~pop_density, y = ~cases,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
# pop_density vs. deathsper100k
cv_states_today_filter %>%
plot_ly(x = ~pop_density, y = ~deathsper100k,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
# Adding hoverinfo
cv_states_today_filter %>%
plot_ly(x = ~pop_density, y = ~deathsper100k,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5),
hoverinfo = 'text',
text = ~paste( paste(state, ":", sep=""), paste(" Cases per 100k: ", per100k, sep="") ,
paste(" Deaths per 100k: ", deathsper100k, sep=""), sep = "<br>")) %>%
layout(title = "Population-normalized COVID-19 deaths (per 100k) vs. population density for US states",
yaxis = list(title = "Deaths per 100k"), xaxis = list(title = "Population Density"),
hovermode = "compare")
ggplotly() and geom_smooth()pop_density vs. newdeathsper100k create a chart with the same variables using gglot_ly()geom_smooth()
pop_density is a correlate of newdeathsper100k?### FINISH CODE HERE
p <- ggplot(cv_states_today_filter, aes(x=pop_density, y=deathsper100k, size=population)) + geom_point() + geom_smooth()
ggplotly(p)
naive_CFR for all states over time using plot_ly()
naive_CFR for the states that had an increase in September. How have they changed over time?new_cases and new_deaths together in one plot. Hint: use add_layer()
### FINISH CODE HERE
# Line chart for naive_CFR for all states over time using `plot_ly()`
plot_ly(cv_states, x = ~date, y = ~naive_CFR, color = ~state, type = "scatter", mode = "lines")
### FINISH CODE HERE
# Line chart for Florida showing new_cases and new_deaths together
cv_states %>% filter(state=="Florida") %>% plot_ly(x = ~date, y = ~new_cases, type = "scatter", mode = "lines") %>% add_lines(x = ~date, y = ~new_deaths, type = "scatter", mode = "lines")
Create a heatmap to visualize new_cases for each state on each date greater than June 1st, 2021 - Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes - Use plot_ly() to create a heatmap out of this matrix. Which states stand out? - Repeat with newper100k variable. Now which states stand out? - Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks
### FINISH CODE HERE
# Map state, date, and new_cases to a matrix
library(tidyr)
cv_states_mat <- cv_states %>% select(state, date, new_cases) %>% dplyr::filter(date>=as.Date("2021-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL #get rid of date column
cv_states_mat2 <- as.matrix(cv_states_mat2) #heatmap requires input to be a matrix
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
z=~cv_states_mat2,
type="heatmap",
showscale=T)
# Repeat with newper100k
cv_states_mat <- cv_states %>% select(state, date, newper100k) %>% dplyr::filter(date>=as.Date("2021-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
z=~cv_states_mat2,
type="heatmap",
showscale=T)
# Create a second heatmap after filtering to only include dates every other week
filter_dates <- seq(as.Date("2021-06-15"), as.Date("2021-11-01"), by="2 weeks")
cv_states_mat <- cv_states %>% select(state, date, newdeathsper100k) %>% filter(date %in% filter_dates)
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newdeathsper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
z=~cv_states_mat2,
type="heatmap",
showscale=T)
naive_CFR by state on October 15, 2021naive_CFR by state on most recent datesubplot(). Make sure the shading is for the same range of values (google is your friend for this)### For specified date
pick.date = "2021-10-15"
# Extract the data for each state by its abbreviation
cv_per100 <- cv_states %>% filter(date==pick.date) %>% select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL
# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))
# Set up mapping details
set_map_details <- list(
scope = 'usa',
projection = list(type = 'albers usa'),
showlakes = TRUE,
lakecolor = toRGB('white')
)
# Make sure both maps are on the same color scale
shadeLimit <- 125
# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>%
add_trace(
z = ~newper100k, text = ~hover, locations = ~state,
color = ~newper100k, colors = 'Purples'
)
fig <- fig %>% colorbar(title = paste0("Cases per 100k: ", pick.date), limits = c(0,shadeLimit))
fig <- fig %>% layout(
title = paste('Cases per 100k by State as of ', pick.date, '<br>(Hover for value)'),
geo = set_map_details
)
fig_pick.date <- fig
#############
### Map for today's date
# Extract the data for each state by its abbreviation
cv_per100 <- cv_states_today %>% select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL
# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))
# Set up mapping details
set_map_details <- list(
scope = 'usa',
projection = list(type = 'albers usa'),
showlakes = TRUE,
lakecolor = toRGB('white')
)
# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>%
add_trace(
z = ~newper100k, text = ~hover, locations = ~state,
color = ~newper100k, colors = 'Purples'
)
fig <- fig %>% colorbar(title = paste0("Cases per 100k: ", Sys.Date()), limits = c(0,shadeLimit))
fig <- fig %>% layout(
title = paste('Cases per 100k by State as of', Sys.Date(), '<br>(Hover for value)'),
geo = set_map_details
)
fig_Today <- fig
### Plot together
subplot(fig_pick.date, fig_Today, nrows = 2, margin = .05, which_layout = "merge")